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7 posts

Looplane prompts, instruction precedence, and explicit memory: what the model actually sees

Looplane resolves user and root-to-leaf project instructions before rendering named prompt sections for runtime, skills, workspace state, and the latest 20 explicit memories. The pipeline is traceable and reloadable, but it is not semantic memory and repository text does not become system authority.

OpenAI Wrote 1 Million Lines of Code with Codex: Harness Engineering in Practice

An OpenAI internal team spent 5 months with 3 people and 0 lines of hand-written code, delivering a complete product using Codex. This article distills their core lessons on AGENTS.md design, repo-local knowledge bases, architecture enforcement, and entropy management.

tech guide

Solving Duplicate Config Files for Codex and Claude Code with a Symlink

Claude Code only reads CLAUDE.md; Codex only reads AGENTS.md. Teams using both end up maintaining two identical files. Fix: make CLAUDE.md a symlink pointing to AGENTS.md — one source of truth.

tech guide

Where Should AI Agent Global Skills Live? The Division of Labor Between .claude, Codex Skills, and AGENTS.md

Skill paths are almost always runtime-specific. AGENTS.md is the reliable way to share rules across agents. Put personal reusable capabilities in each agent's supported global directory; put project workflows inside the repo.

OpenClaw Automation, Part 2: Standing Orders Are the Authorization, Automations Are the Clock

Standing orders grant an agent permanent operating authority for a defined program, written into AGENTS.md and injected into every session. They define what it may do; automations define when — and the automation prompt should reference the standing order rather than duplicate it.

How Claude Code Remembers Your Project: CLAUDE.md Layers, Imports, Rules, and Auto Memory

Every Claude Code session starts with a clean context window. Three memory mechanisms carry knowledge across sessions: CLAUDE.md files loaded every session, .claude/rules/ files that load conditionally via paths frontmatter, and auto memory Claude writes itself. All CLAUDE.md layers are concatenated into context — not inherited by override. This guide covers layer behavior, @path imports, monorepo strategies with nested CLAUDE.md, and sharing one instruction file across tools via @AGENTS.md.

Claude Code's Three-Layer Quality Defense: Hooks, Skills, and Instruction Files

Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.